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Deep learning in breast imaging.

Arka Bhowmik1, Sarah Eskreis-Winkler1

  • 1Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.

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Deep learning (DL) shows promise in improving breast cancer screening accuracy and efficiency. Further clinical trials and regulatory frameworks are needed for widespread adoption of these AI tools in breast imaging.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer screening is crucial for reducing morbidity and mortality.
  • Current breast imaging interpretation is subjective, time-consuming, and prone to error.
  • Deep learning (DL) offers potential solutions to enhance breast imaging analysis.

Purpose of the Study:

  • To review the fundamentals of DL in breast imaging.
  • To describe current DL applications in breast cancer detection and risk prediction.
  • To discuss challenges and future directions for AI in breast cancer diagnostics.

Main Methods:

  • Review of existing literature on deep learning in breast imaging.
  • Analysis of retrospective and small reader studies.
  • Discussion of prospective trial needs and regulatory considerations.

Main Results:

  • Deep learning models demonstrate potential for human-level or superior performance in medical imaging tasks.
  • DL may automate screening, improve cancer detection, reduce unnecessary biopsies, and aid risk assessment.
  • AI applications show promise in disease prognostication.

Conclusions:

  • Deep learning holds significant potential to revolutionize breast cancer screening and diagnosis.
  • Prospective validation and development of regulatory frameworks are essential for clinical implementation.
  • AI-driven tools could enhance efficiency, accuracy, and patient outcomes in breast imaging.